Företagsakademin, Henriksgatan 7 FIN-20500 Åbo Exploratory Research Design Secondary Data Qualitative Research Survey & Observation Experiments

Företagsakademin, Henriksgatan 7 FIN-20500 Åbo

Exploratory Research Design

Secondary DataQualitative ResearchSurvey & Observation

Experiments


Primary vs. Secondary data• Primary data:

originated by the researcher for the specific purpose addressing the research problem

• Secondary data: data collected for some other purpose than the problm at hand

PrimaryData

Secon-daryData

CollectionPurpose

For the purpose at hand

For other problems

CollectionProcess

Very involved

Rapid and easy

CollectionCost

High Relatively low

CollectionTime

Long Short


Advantages and uses of SD1. Identifying the

problem2. Better define the

problem3. Develop and approach

to the problem4. Formulate an

appropriate research design

5. Answering certain research Qs and test some hypotheses

6. Interpret primary data more insightfully

• Rule: Examination of available SD is a prerequisite to the collection of primary data. Start with SD; collect PD only after the SD source yield marginal returns


Criteria for evaluating SD• Specifications/

Methodology– Data collection method– Response rate– Quality of data– Sampling technique– Sample size– Questionnaire design– Field work– Data analysis

• Data should be systematicsystematic, valid, and generalizable to the problem at hand.

• Error/Accuracy– Examine errors in:

Approach, Research design, Sampling, Data collection, Data analysis and Reporting

• Assess accuracy by comparing data from different sources.

• Currency– Time lag between

collection and publication. Frequency of updates

– Census data is periodically updated by syndicated firms


Criteria for evaluating SD• Objective: Why were

the data collected, i.e. determine the relevance of the data

• Nature: Definition of key variables, units of measurement, categories used, relationships examined, i.e. reconfigure the data to increase their usefulness if possible.

• Dependability: Expertise, credibility, reputation, and trustworthiness of the source, i.e. Data should be obtained from an original rather than an acquired source


Classification of SD• Internal

– Ready to use– Requires further

processing

• External– Published materials

• guides• directories• indexes• nongov’t statistical

data• Census data

– Computerized databases

• Online/offline, internet DB, bibliograhic DB

– Syndicated services

• surveys• panels• scanner services• audits• industry services


A classification of Marketing research data

Secondary Data

Qualitative Data

Survey Data Observational and other Data

Descriptive

Experimental Data

Causal

Quantitative Data

Primary Data

Marketing Research Data


Primary Data: Qualitative versus Quantitative Research• Qualitative

research– An unstructured,

exploratory research methodology based on small samples that provide insights and understanding of the problem setting

• Quantitative research– A research methodology

that seeks to quantify the data and, typically applies some form of statistical analysis

• Qualitative research procedures:– Direct, indirect– Focus group: an

interview conducted by a trained moderator, a small group, unstructured

– Association techniqueword associations

– Completion techniques• sentence completion• story completion

– Construction techniques• picture response• cartoon test

– Expressive techniques


Focus Groups, Depth Interviews, projective techniques

Criteria Focus Groups

Depth Interviews

Projective techniques

Structure High Medium Low

Probing of respondent

Low High Medium

Moderator bias Medium High Low to High

Interpretation bias

Low Medium High

Subconscious info

Low Medium to high High

Innovative info High Medium Low

Sensitive info Low Medium High

Unusual behavior

No Some Yes

Usefulness Highly useful Useful Somewhat


Qualitative versus Quantitative Research

Qualitative Research

Quantitative Research

Objective To gain a qualitative understanding of the underlying reasons and motivations

To quantify the data and generalize the results from the sample to the population of interest

Sample Small number of non-representative cases

Large number of representative cases

Data collection

Unstructured Structured

Data analysis Non-statistical Statistical

Outcome Develop an initial understanding

Recommend a final course of action


Descriptive Research Design: Survey & Observation• Survey: A structured

questionnaire given to respondents and designed to elicit specific information

• Structured data collection: Use of formal questionnaire that presents questions in a prearranged order

• Fixed-alternative questions: Respondents are required to choose from a set of predetermined answers

• Survey Methods:– Telephone;

• traditional, computer-assisted

– Personal interviewing;

• In-home, Mall Intercept, computer-assisted

– Mail Interviewing;• mail, mail panel

– Electronic Intrvieweing;

• E-mail, Internet


Descriptive Research Design: Survey & Observation• Observation: the

recording of behavioral patterna of people, objects, and events in a systematic manner to obtain information about the phenomenon of interest

• Structured observation: observation techniques where the researcher defines the behavior to be observed and the methods by which they will be measured

• Unstructured observations: A researcher monitors all aspects of the phenomenon without specifying the details in advance

• Natural observations: observing behavior as it takes place in the environment

• Contrived observation: the behavior is observed in an artificial environment

• Disguised vs. undisguised observation


Causal Research Design: Experimentation• Causality:

Ordinary meaning and scientific meaning:

• Ordinary:– X is the only

cause of Y– X must always

lead to Y– It is possible to

prove that X is a cause of Y

• Scientific:– X is only one of a

number of possible causes of Y

– The occurence of X makes the occurence of Y more probable

– We can never prove the X is a cause of Y. At best we can infer that X is a cause of Y


Causal Research Design: Experimentation• Concomitant Variation: The

extent to which a cause X and an effect Y occur together is predicted by the hypothesis under consideration– Purchase of Fashion Clothing Y is

influenced by education X• The absence of initial evidence

of concomitant variation does not imply there is no causation


Can we prove high education cause high purchase?Education X

Purchase of Fashion Clothing Y

High Low Total

High 363 (73%)

137 (27%)

500 (100%)

Low 322 (64%)

178 (36%)

500 (100%)


What about Income?Low Income High Income

Purchase Purchase

Educa-tion

High Low Total Educa-tion

High Low Total

High 122(61%)

78(39%)

200(100%)

High 241(80%)

59(20%)

300(100%)

Low 171(57%)

129(43%)

300(100%)

Low 151(76%)

49(24%)

200(100%)


Time order of occurence of variables• The causing event

must occur either before or simultaneously with the effect. It cannot occur afterwards!

• An effect cannot be produced by an event that occur after the effect has taken place!

• But: A variable can be both the cause and the effect in a causal relationship!

• Customer who shop frequently in a store is likely to have a credit card for that store. Also customers who have the card is likely to shop there more frequently


Definitions and Concepts

• Independent variables (treatments) (iv) are variables that are manipulated by the researcher and whose effects are measured and compared. These can be price levels, package designs, advertising themes, etc.

• Test unit are individuals, organizations, or other entities whose response to the independnt variables or treatments is being examined. Test units may include consumers, stores, geographic areas



• Dependent variables (dv) are the variables that measure the effect of the independent variable on the test unit. These variables may include, sales, profits, market share, brand, consumer choice, intentions to buy, etc.

• Extraneous or confounding variables are all variables other than the independent variables that affect the response of the test units. These variables can confound the dv. measures in a way that weakens or invalidates the results, e.g. store size, location, competitive effort. These variables have to be controlled for!



• Experiment is formed when the researcher manipulates one or more iv. and measures their effect on one or more dv. while controlling for the effect of confounding variables

• Experimental design is a set of procedures specifying– the test units and how they are to be divided

into homogeneous subsamples– what iv is to be manipulated– what dv are to be measured– how the confounding variabls are to be

controlled


Validity

• Internal validity is a measure of accuracy of an experiment. It measures whether an iv actually causes the effects on the dv

• External validity determines whether the cause-and-effect found in the experiment can be generalized


Confounding variables• History: events that

are external to the experiment but occur a the same time

• Maturation: changes in the test unit due to passage of time

• Testing effect:– main testing effect– interactive testing

effect

• Main testing effect: when a prior observation affects the latter. Affects the internal validity

• interactive testing effect: prior measurement affects the test unit’s response to the iv. Affects the external validity


Confounding variables

• Statistical regression: when respondents with extreme scores move closer to the average, i.e. the change in attitude is attributable to statistical regression rather than the treatment

• Selection bias: Improper assignment of test units to treatment

• Mortality: loss of a test unit while the experiment is in progress


Controlling confounding variables• Randomization• Matching• Statistical control: measuring

the confounding variable and adjusting for their effects

• Design control


Classification of experimental designs

Preexprimental True Experimental Quasi experimental Statistical

Experimetal designs

One-shot casestudy

One grouppre-posttest

Static group

Pretest-posttestcontrol group

Posttest-onlycontrol group

SolomonFour group

Time Series

Multiple Time Series

RandomizedBlocks

Latin Square

Factorial


Classification of experimental designs• Preexperimental

design do not control for confounding factors by randomization

• True experimental designs the researcher can randomly assign test units to experimental groups and assign treatment randomly to experimental groups

• quasi-experimental design is like the true experimental design BUT without full experimental control


Limitations of experimentation• Time• Cost• Administration• Application: Test marketing

– controlled test market– simulated test market

Documents

Företagsakademin, Henriksgatan 7 FIN-20500 Åbo Exploratory Research Design Secondary Data Qualitative Research Survey & Observation Experiments